Profiling an application for power consumption during execution on a compute node

ABSTRACT

Methods, apparatus, and products are disclosed for profiling an application for power consumption during execution on a compute node that include: receiving an application for execution on a compute node; identifying a hardware power consumption profile for the compute node, the hardware power consumption profile specifying power consumption for compute node hardware during performance of various processing operations; determining a power consumption profile for the application in dependence upon the application and the hardware power consumption profile for the compute node; and reporting the power consumption profile for the application.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation application of and claims priorityfrom U.S. patent application Ser. No. 12/167,302, filed on Jul. 3, 2008.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with Government support under Contract No.B554331 awarded by the Department of Energy. The Government has certainrights in this invention.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The field of the invention is data processing, or, more specifically,methods, apparatus, and products for profiling an application for powerconsumption during execution on a compute node.

2. Description of Related Art

The development of the EDVAC computer system of 1948 is often cited asthe beginning of the computer era. Since that time, computer systemshave evolved into extremely complicated devices. Today's computers aremuch more sophisticated than early systems such as the EDVAC. Computersystems typically include a combination of hardware and softwarecomponents, application programs, operating systems, processors, buses,memory, input/output (‘I/O’) devices, and so on. As advances insemiconductor processing and computer architecture push the performanceof the computer higher and higher, more sophisticated computer softwarehas evolved to take advantage of the higher performance of the hardware,resulting in computer systems today that are much more powerful thanjust a few years ago.

Parallel computing is an area of computer technology that hasexperienced advances. Parallel computing is the simultaneous executionof the same task (split up and specially adapted) on multiple processorsin order to obtain results faster. Parallel computing is based on thefact that the process of solving a problem usually can be divided intosmaller tasks, which may be carried out simultaneously with somecoordination.

Parallel computers execute applications that include both parallelalgorithms and serial algorithms. A parallel algorithm can be split upto be executed a piece at a time on many different processing devices,and then put back together again at the end to get a data processingresult. Some algorithms are easy to divide up into pieces. Splitting upthe job of checking all of the numbers from one to a hundred thousand tosee which are primes could be done, for example, by assigning a subsetof the numbers to each available processor, and then putting the list ofpositive results back together. In this specification, the multipleprocessing devices that execute the algorithms of an application arereferred to as ‘compute nodes.’ A parallel computer is composed ofcompute nodes and other processing nodes as well, including, forexample, input/output (‘I/O’) nodes, and service nodes.

Parallel algorithms are valuable because it is faster to perform somekinds of large computing tasks via a parallel algorithm than it is via aserial (non-parallel) algorithm, because of the way modern processorswork. It is far more difficult to construct a computer with a singlefast processor than one with many slow processors with the samethroughput. There are also certain theoretical limits to the potentialspeed of serial processors. On the other hand, every parallel algorithmhas a serial part and so parallel algorithms have a saturation point.After that point adding more processors does not yield any morethroughput but only increases the overhead and cost.

Parallel algorithms are designed also to optimize one more resource—thedata communications requirements among the nodes of a parallel computer.There are two ways parallel processors communicate, shared memory ormessage passing. Shared memory processing needs additional locking forthe data and imposes the overhead of additional processor and bus cyclesand also serializes some portion of the algorithm.

Message passing processing uses high-speed data communications networksand message buffers, but this communication adds transfer overhead onthe data communications networks as well as additional memory need formessage buffers and latency in the data communications among nodes.Designs of parallel computers use specially designed data communicationslinks so that the communication overhead will be small but it is theparallel algorithm that decides the volume of the traffic.

Many data communications network architectures are used for messagepassing among nodes in parallel computers. Compute nodes may beorganized in a network as a ‘torus’ or ‘mesh,’ for example. Also,compute nodes may be organized in a network as a tree. A torus networkconnects the nodes in a three-dimensional mesh with wrap around links.Every node is connected to its six neighbors through this torus network,and each node is addressed by its x,y,z coordinate in the mesh. In sucha manner, a torus network lends itself to point to point operations. Ina tree network, the nodes typically are organized in a binary treearrangement: each node has a parent and two children (although somenodes may only have zero children or one child, depending on thehardware configuration). In computers that use a torus and a treenetwork, the two networks typically are implemented independently of oneanother, with separate routing circuits, separate physical links, andseparate message buffers. A tree network provides high bandwidth and lowlatency for certain collective operations, such as, for example, anallgather, allreduce, broadcast, scatter, and so on.

When processing an application, the compute nodes typically do notutilize the nodes' hardware components uniformly for each portion of theapplication. For example, during a portion of the application thatperforms a collective operation, the compute nodes typically utilize thenodes' network components that interface with the tree network but donot utilize the components that interface with the torus network. Duringa portion of the application that performs mathematical operations onintegers, the compute nodes typically do not need to utilize thefloat-point units of the nodes' processors. The manner in which thenodes' hardware components are utilized to process the differentportions of the application determine the overall power consumption ofthe nodes while executing the application. Having information on how thecompute nodes consume power while executing an application may helpapplication developers efficiently reduce the power consumption of theapplication, thereby conserving valuable computing resources.

SUMMARY OF THE INVENTION

Methods, apparatus, and products are disclosed for profiling anapplication for power consumption during execution on a compute nodethat include: receiving an application for execution on a compute node;identifying a hardware power consumption profile for the compute node,the hardware power consumption profile specifying power consumption forcompute node hardware during performance of various processingoperations; determining a power consumption profile for the applicationin dependence upon the application and the hardware power consumptionprofile for the compute node; and reporting the power consumptionprofile for the application.

The foregoing and other objects, features and advantages of theinvention will be apparent from the following more particulardescriptions of exemplary embodiments of the invention as illustrated inthe accompanying drawings wherein like reference numbers generallyrepresent like parts of exemplary embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary system for profiling an application forpower consumption during execution on a compute node according toembodiments of the present invention.

FIG. 2 sets forth a block diagram of an exemplary compute node useful ina parallel computer capable of profiling an application for powerconsumption during execution on a compute node according to embodimentsof the present invention.

FIG. 3A illustrates an exemplary Point To Point Adapter useful insystems capable of profiling an application for power consumption duringexecution on a compute node according to embodiments of the presentinvention.

FIG. 3B illustrates an exemplary Global Combining Network Adapter usefulin systems capable of profiling an application for power consumptionduring execution on a compute node according to embodiments of thepresent invention.

FIG. 4 sets forth a line drawing illustrating an exemplary datacommunications network optimized for point to point operations useful insystems capable of profiling an application for power consumption duringexecution on a compute node in accordance with embodiments of thepresent invention.

FIG. 5 sets forth a line drawing illustrating an exemplary datacommunications network optimized for collective operations useful insystems capable of profiling an application for power consumption duringexecution on a compute node in accordance with embodiments of thepresent invention.

FIG. 6 sets forth a flow chart illustrating an exemplary method forprofiling an application for power consumption during execution on acompute node according to embodiments of the present invention.

FIG. 7 sets forth a flow chart illustrating a further exemplary methodfor profiling an application for power consumption during execution on acompute node according to embodiments of the present invention.

FIG. 8 sets forth a flow chart illustrating a further exemplary methodfor profiling an application for power consumption during execution on acompute node according to embodiments of the present invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Exemplary methods, apparatus, and computer program products forprofiling an application for power consumption during execution on acompute node according to embodiments of the present invention aredescribed with reference to the accompanying drawings, beginning withFIG. 1. FIG. 1 illustrates an exemplary system for profiling anapplication (200) for power consumption during execution on a computenode (100) according to embodiments of the present invention. The systemof FIG. 1 includes a parallel computer (100), non-volatile memory forthe computer in the form of data storage device (118), an output devicefor the computer in the form of printer (120), and an input/outputdevice for the computer in the form of computer terminal (122). Parallelcomputer (100) in the example of FIG. 1 includes a plurality of computenodes (102) that execute an application (200). The application (200) ofFIG. 1 is a set of computer program instructions that provide user-leveldata processing.

Each compute node (102) of FIG. 1 may include a plurality of processorsfor use in executing an application on the parallel computer (100)according to embodiments of the present invention. The processors ofeach compute node (102) in FIG. 1 are operatively coupled to computermemory such as, for example, random access memory (‘RAM’). Each computenode (102) may operate in several distinct modes that affect therelationship among the processors and the memory on that node such as,for example, serial processing mode or parallel processing mode. Themode in which the compute nodes operate is generally set during thenode's boot processes and does not change until the node reboots.

In serial processing mode, often referred to a ‘virtual node mode,’ theprocessors of a compute node operate independently of one another, andeach processor has access to a partition of the node's total memory thatis exclusively dedicated to that processor. For example, if a computenode has four processors and two Gigabytes (GB) of RAM, when operatingin serial processing mode, each processor may process a threadindependently of the other processors on that node, and each processormay access a 512 Megabyte (MB) portion of that node's total 2 GB of RAM.

In parallel processing mode, often referred to as ‘symmetricmulti-processing mode,’ one of the processors acts as a master, and theremaining processors serve as slaves to the master processor. Eachprocessor has access to the full range of computer memory on the computenode. Continuing with the exemplary node above having four processorsand 2 GB of RAM, for example, each slave processor may cooperativelyprocess threads spawned from the master processor, and all of theprocessors have access to the node's entire 2 GB of RAM.

The compute nodes (102) are coupled for data communications by severalindependent data communications networks including a Joint Test ActionGroup (‘JTAG’) network (104), a global combining network (106) which isoptimized for collective operations, and a torus network (108) which isoptimized point to point operations. The global combining network (106)is a data communications network that includes data communications linksconnected to the compute nodes so as to organize the compute nodes as atree. Each data communications network is implemented with datacommunications links among the compute nodes (102). The datacommunications links provide data communications for parallel operationsamong the compute nodes of the parallel computer. The links betweencompute nodes are bi-directional links that are typically implementedusing two separate directional data communications paths.

In addition, the compute nodes (102) of parallel computer are organizedinto at least one operational group (132) of compute nodes forcollective parallel operations on parallel computer (100). Anoperational group of compute nodes is the set of compute nodes uponwhich a collective parallel operation executes. Collective operationsare implemented with data communications among the compute nodes of anoperational group. Collective operations are those functions thatinvolve all the compute nodes of an operational group. A collectiveoperation is an operation, a message-passing computer programinstruction that is executed simultaneously, that is, at approximatelythe same time, by all the compute nodes in an operational group ofcompute nodes. Such an operational group may include all the computenodes in a parallel computer (100) or a subset all the compute nodes.Collective operations are often built around point to point operations.A collective operation requires that all processes on all compute nodeswithin an operational group call the same collective operation withmatching arguments. A ‘broadcast’ is an example of a collectiveoperation for moving data among compute nodes of an operational group. A‘reduce’ operation is an example of a collective operation that executesarithmetic or logical functions on data distributed among the computenodes of an operational group. An operational group may be implementedas, for example, an MPI ‘communicator.’

‘MPI’ refers to ‘Message Passing Interface,’ a prior art parallelcommunications library, a module of computer program instructions fordata communications on parallel computers. Examples of prior-artparallel communications libraries that may be improved for use withsystems according to embodiments of the present invention include MPIand the ‘Parallel Virtual Machine’ (‘PVM’) library. PVM was developed bythe University of Tennessee, The Oak Ridge National Laboratory, andEmory University. MPI is promulgated by the MPI Forum, an open groupwith representatives from many organizations that define and maintainthe MPI standard. MPI at the time of this writing is a de facto standardfor communication among compute nodes running a parallel program on adistributed memory parallel computer. This specification sometimes usesMPI terminology for ease of explanation, although the use of MPI as suchis not a requirement or limitation of the present invention.

Some collective operations have a single originating or receivingprocess running on a particular compute node in an operational group.For example, in a ‘broadcast’ collective operation, the process on thecompute node that distributes the data to all the other compute nodes isan originating process. In a ‘gather’ operation, for example, theprocess on the compute node that received all the data from the othercompute nodes is a receiving process. The compute node on which such anoriginating or receiving process runs is referred to as a logical root.

Most collective operations are variations or combinations of four basicoperations: broadcast, gather, scatter, and reduce. The interfaces forthese collective operations are defined in the MPI standards promulgatedby the MPI Forum. Algorithms for executing collective operations,however, are not defined in the MPI standards. In a broadcast operation,all processes specify the same root process, whose buffer contents willbe sent. Processes other than the root specify receive buffers. Afterthe operation, all buffers contain the message from the root process.

In a scatter operation, the logical root divides data on the root intosegments and distributes a different segment to each compute node in theoperational group. In scatter operation, all processes typically specifythe same receive count. The send arguments are only significant to theroot process, whose buffer actually contains sendcount*N elements of agiven data type, where N is the number of processes in the given groupof compute nodes. The send buffer is divided and dispersed to allprocesses (including the process on the logical root). Each compute nodeis assigned a sequential identifier termed a ‘rank.’ After theoperation, the root has sent sendcount data elements to each process inincreasing rank order. Rank 0 receives the first sendcount data elementsfrom the send buffer. Rank 1 receives the second sendcount data elementsfrom the send buffer, and so on.

A gather operation is a many-to-one collective operation that is acomplete reverse of the description of the scatter operation. That is, agather is a many-to-one collective operation in which elements of adatatype are gathered from the ranked compute nodes into a receivebuffer in a root node.

A reduce operation is also a many-to-one collective operation thatincludes an arithmetic or logical function performed on two dataelements. All processes specify the same ‘count’ and the same arithmeticor logical function. After the reduction, all processes have sent countdata elements from computer node send buffers to the root process. In areduction operation, data elements from corresponding send bufferlocations are combined pair-wise by arithmetic or logical operations toyield a single corresponding element in the root process's receivebuffer. Application specific reduction operations can be defined atruntime. Parallel communications libraries may support predefinedoperations. MPI, for example, provides the following pre-definedreduction operations:

MPI_MAX maximum MPI_MIN minimum MPI_SUM sum MPI_PROD product MPI_LANDlogical and MPI_BAND bitwise and MPI_LOR logical or MPI_BOR bitwise orMPI_LXOR logical exclusive or MPI_BXOR bitwise exclusive or

In addition to compute nodes, the parallel computer (100) includesinput/output (‘I/O’) nodes (110, 114) coupled to compute nodes (102)through the global combining network (106). The compute nodes in theparallel computer (100) are partitioned into processing sets such thateach compute node in a processing set is connected for datacommunications to the same I/O node. Each processing set, therefore, iscomposed of one I/O node and a subset of compute nodes (102). The ratiobetween the number of compute nodes to the number of I/O nodes in theentire system typically depends on the hardware configuration for theparallel computer. For example, in some configurations, each processingset may be composed of eight compute nodes and one I/O node. In someother configurations, each processing set may be composed of sixty-fourcompute nodes and one I/O node. Such example are for explanation only,however, and not for limitation. Each I/O nodes provide I/O servicesbetween compute nodes (102) of its processing set and a set of I/Odevices. In the example of FIG. 1, the I/O nodes (110, 114) areconnected for data communications I/O devices (118, 120, 122) throughlocal area network (‘LAN’) (130) implemented using high-speed Ethernet.

The parallel computer (100) of FIG. 1 also includes a service node (116)coupled to the compute nodes through one of the networks (104). Servicenode (116) provides services common to pluralities of compute nodes,administering the configuration of compute nodes, loading programs intothe compute nodes, starting program execution on the compute nodes,retrieving results of program operations on the computer nodes, and soon. Service node (116) runs a service application (124) and communicateswith users (128) through a service application interface (126) that runson computer terminal (122).

The service node (116) of FIG. 1 also has installed upon it a powerprofiling module (140). The power profiling module (140) of FIG. 1 is aset of computer program instructions capable of profiling an applicationfor power consumption during execution on a compute node according toembodiments of the present invention. The power profiling module (140)of FIG. 1 operates generally for profiling an application for powerconsumption during execution on a compute node according to embodimentsof the present invention by: receiving the application (200) forexecution on a compute node; identifying a hardware power consumptionprofile (142) for the compute node, the hardware power consumptionprofile (142) specifying power consumption for compute node hardwareduring performance of various processing operations; determining a powerconsumption profile (144) for the application (200) in dependence uponthe application (200) and the hardware power consumption profile (142)for the compute node; and reporting the power consumption profile (144)for the application (200).

The power consumption profile (144) of FIG. 1 for the application (200)is a data structure that associates power consumption with particularportions of the application (200). As mentioned above, differentportions of an application may utilize compute node hardware indifferent ways, thereby resulting in different levels of powerconsumption based on the application portion being executed. Powerconsumption for compute node hardware may be specified in units ofWatts, some other units of power, or in the units of some other quantityhaving a known relationship to power such as, for example, energy, CPUcycles, and so on. The values used to represent power consumption in thepower consumption profile (144) may be actual measured values orestimated values based on measured values.

The hardware power consumption profile (142) of FIG. 1 represents a datastructure that specifies power consumption for compute node hardwareduring performance of various processing operations. A processingoperation is a task specified by an application or performed by acompute node in response to a task specified by an application. Such atask may be a single instruction or a set of multiple instructions.

For example, a processing operation may be implemented as a single floatmultiply-add instruction or a series of instructions processed when acache miss occurs during application execution. Compute node hardwaremay include, for example, a compute node's processors, computer memory,adapter circuitry, and other hardware as will occur those of skill inthe art.

Readers will note that the hardware power consumption profile (142)provides power consumption information of various processing operationsfor platform-specific hardware. In such a manner, the service node (116)may use the hardware power consumption profile (142) to translateprocessing operations specified by the application (200) into powerconsumption information for various portions of the application (200)executing on the platform-specific hardware. Using different hardwarepower consumption profiles, the service node (116) may obtain differentpower consumption profiles for the application (200) on the differenthardware platforms even though the processing operations specified bythe application (200) may not change.

Using the power consumption profile (144) for the application (200), anapplication developer may discover that certain portions of theapplication (200) consume power at undesirable levels. Accordingly,profiling an application for power consumption during execution on acompute node according to embodiments of the present invention may alsoinclude: altering the application (200) in dependence upon the powerconsumption profile (144) for the application (200) and executing thealtered application on the compute node.

In the example of FIG. 1, the plurality of compute nodes (102) areimplemented in a parallel computer (100) and are connected togetherusing a plurality of data communications networks (104, 106, 108). Thepoint to point network (108) is optimized for point to point operations.The global combining network (106) is optimized for collectiveoperations. Although profiling an application for power consumptionduring execution on a compute node according to embodiments of thepresent invention is described above in terms of an architecture for aparallel computer, readers will note that such an embodiment is forexplanation only and not for limitation. In fact, profiling anapplication for power consumption during execution on a compute nodeaccording to embodiments of the present invention may be implementedusing a variety of computer system architectures, including for examplearchitectures for a stand-alone compute node, a cluster of nodes, adistributed computing system, a grid computing system, and so on.

The arrangement of nodes, networks, and I/O devices making up theexemplary system illustrated in FIG. 1 are for explanation only, not forlimitation of the present invention. Data processing systems capable ofprofiling an application for power consumption during execution on acompute node according to embodiments of the present invention mayinclude additional nodes, networks, devices, and architectures, notshown in FIG. 1, as will occur to those of skill in the art. Althoughthe parallel computer (100) in the example of FIG. 1 includes sixteencompute nodes (102), readers will note that parallel computers capableof profiling an application for power consumption during execution on acompute node according to embodiments of the present invention mayinclude any number of compute nodes. In addition to Ethernet and JTAG,networks in such data processing systems may support many datacommunications protocols including for example TCP (Transmission ControlProtocol), IP (Internet Protocol), and others as will occur to those ofskill in the art. Various embodiments of the present invention may beimplemented on a variety of hardware platforms in addition to thoseillustrated in FIG. 1.

Profiling an application for power consumption during execution on acompute node according to embodiments of the present invention may beimplemented on a parallel computer, among other types of exemplarysystems. Such parallel computers may include thousands of such computenodes. Each compute node is in turn itself a kind of computer composedof one or more computer processors, its own computer memory, and its owninput/output adapters. For further explanation, therefore, FIG. 2 setsforth a block diagram of an exemplary compute node (152) useful in aparallel computer capable of profiling an application for powerconsumption during execution on a compute node according to embodimentsof the present invention. The compute node (152) of FIG. 2 includes oneor more computer processors (164) as well as random access memory(‘RAM’) (156). The processors (164) are connected to RAM (156) through ahigh-speed memory bus (154) and through a bus adapter (194) and anextension bus (168) to other components of the compute node (152).Stored in RAM (156) of FIG. 2 is an application (200). The application(200) is a set of computer program instructions that provide user-leveldata processing.

Also stored in RAM (156) is a power profiling module (140), a set ofcomputer program instructions capable of profiling an application forpower consumption during execution on a compute node according toembodiments of the present invention. The power profiling module (140)of FIG. 2 operates generally for profiling an application for powerconsumption during execution on a compute node according to embodimentsof the present invention by: receiving the application (200) forexecution on a compute node; identifying a hardware power consumptionprofile (142) for the compute node, the hardware power consumptionprofile (142) specifying power consumption for compute node hardwareduring performance of various processing operations; determining a powerconsumption profile (144) for the application (200) in dependence uponthe application (200) and the hardware power consumption profile (142)for the compute node; and reporting the power consumption profile (144)for the application (200).

Also stored RAM (156) is a messaging module (161), a library of computerprogram instructions that carry out parallel communications amongcompute nodes, including point to point operations as well as collectiveoperations. User-level applications such as application (200) effectdata communications with other applications running on other computenodes by calling software routines in the messaging modules (161). Alibrary of parallel communications routines may be developed fromscratch for use in systems according to embodiments of the presentinvention, using a traditional programming language such as the Cprogramming language, and using traditional programming methods to writeparallel communications routines. Alternatively, existing prior artlibraries may be used such as, for example, the ‘Message PassingInterface’ (‘MPI’) library, the ‘Parallel Virtual Machine’ (‘PVM’)library, and the Aggregate Remote Memory Copy Interface (‘ARMCI’)library.

Also stored in RAM (156) is an operating system (162), a module ofcomputer program instructions and routines for an application program'saccess to other resources of the compute node. It is typical for anapplication program and parallel communications library in a computenode of a parallel computer to run a single thread of execution with nouser login and no security issues because the thread is entitled tocomplete access to all resources of the node. The quantity andcomplexity of tasks to be performed by an operating system on a computenode in a parallel computer therefore are smaller and less complex thanthose of an operating system on a serial computer with many threadsrunning simultaneously. In addition, there is no video I/O on thecompute node (152) of FIG. 2, another factor that decreases the demandson the operating system. The operating system may therefore be quitelightweight by comparison with operating systems of general purposecomputers, a pared down version as it were, or an operating systemdeveloped specifically for operations on a particular parallel computer.Operating systems that may usefully be improved, simplified, for use ina compute node include UNIX™, Linux™, Microsoft Vista™, AIX™, IBM'si5/OS™, and others as will occur to those of skill in the art.

The operating system (162) of FIG. 2 includes a performance monitor(212). The performance monitor (212) is a service of the operatingsystem (162) that monitors the performance characteristics of thecompute node (152) and provides those performance characteristics to thepower profiling module (140). The performance monitor (212) monitors theperformance characteristics of the compute node (152) by receivinginformation from the components of the compute node (152) and fromvarious sensors and detectors (not shown) that measure certainperformance aspects of those components' operation. For example, theperformance monitor (212) may maintain a counter that tracks the numberof floating point operations performed by the processors (164). Theperformance monitor (212) may also retrieve voltage and current measuresfrom a voltage regulator that provides power processors (164) or thememory modules implementing the RAM (156). The performance monitor (212)may communicate with the components of the compute node (152) throughthe processor (164) or a service processor (not shown) that connects toeach of the hardware components. Such connections may be implementedusing the buses (154, 168) illustrated in FIG. 2 or through out of bandbuses (not shown) such as, for example, an Inter-Integrated Circuit(‘I2C’) bus, a JTAG network, a System Management Bus (‘SMBus’), and soon. The performance monitor (212) may provide an application programminginterface (‘API’) through which other operating system software modulesor software components not part of the operating system (162) may accessor subscribe to the performance monitoring services provided by theperformance monitor (212).

The exemplary compute node (152) of FIG. 2 includes severalcommunications adapters (172, 176, 180, 188) for implementing datacommunications with other nodes of a parallel computer. Such datacommunications may be carried out serially through RS-232 connections,through external buses such as USB, through data communications networkssuch as IP networks, and in other ways as will occur to those of skillin the art. Communications adapters implement the hardware level of datacommunications through which one computer sends data communications toanother computer, directly or through a network. Examples ofcommunications adapters useful in systems for profiling an applicationfor power consumption during execution on a compute node according toembodiments of the present invention include modems for wiredcommunications, Ethernet (IEEE 802.3) adapters for wired networkcommunications, and 802.11b adapters for wireless networkcommunications.

The data communications adapters in the example of FIG. 2 include aGigabit Ethernet adapter (172) that couples example compute node (152)for data communications to a Gigabit Ethernet (174). Gigabit Ethernet isa network transmission standard, defined in the IEEE 802.3 standard,that provides a data rate of 1 billion bits per second (one gigabit).Gigabit Ethernet is a variant of Ethernet that operates over multimodefiber optic cable, single mode fiber optic cable, or unshielded twistedpair.

The data communications adapters in the example of FIG. 2 includes aJTAG Slave circuit (176) that couples example compute node (152) fordata communications to a JTAG Master circuit (178). JTAG is the usualname used for the IEEE 1149.1 standard entitled Standard Test AccessPort and Boundary-Scan Architecture for test access ports used fortesting printed circuit boards using boundary scan. JTAG is so widelyadapted that, at this time, boundary scan is more or less synonymouswith JTAG. JTAG is used not only for printed circuit boards, but alsofor conducting boundary scans of integrated circuits, and is also usefulas a mechanism for debugging embedded systems, providing a convenient“back door” into the system. The example compute node of FIG. 2 may beall three of these: It typically includes one or more integratedcircuits installed on a printed circuit board and may be implemented asan embedded system having its own processor, its own memory, and its ownI/O capability. JTAG boundary scans through JTAG Slave (176) mayefficiently configure processor registers and memory in compute node(152) for use in profiling an application for power consumption duringexecution on a compute node according to embodiments of the presentinvention.

The data communications adapters in the example of FIG. 2 includes aPoint To Point Adapter (180) that couples example compute node (152) fordata communications to a network (108) that is optimal for point topoint message passing operations such as, for example, a networkconfigured as a three-dimensional torus or mesh. Point To Point Adapter(180) provides data communications in six directions on threecommunications axes, x, y, and z, through six bidirectional links: +x(181), −x (182), +y (183), −y (184), +z (185), and −z (186).

The data communications adapters in the example of FIG. 2 includes aGlobal Combining Network Adapter (188) that couples example compute node(152) for data communications to a network (106) that is optimal forcollective message passing operations on a global combining networkconfigured, for example, as a binary tree. The Global Combining NetworkAdapter (188) provides data communications through three bidirectionallinks: two to children nodes (190) and one to a parent node (192).

Example compute node (152) includes two arithmetic logic units (‘ALUs’).ALU (166) is a component of processor (164), and a separate ALU (170) isdedicated to the exclusive use of Global Combining Network Adapter (188)for use in performing the arithmetic and logical functions of reductionoperations. Computer program instructions of a reduction routine inparallel communications library (160) may latch an instruction for anarithmetic or logical function into instruction register (169). When thearithmetic or logical function of a reduction operation is a ‘sum’ or a‘logical or,’ for example, Global Combining Network Adapter (188) mayexecute the arithmetic or logical operation by use of ALU (166) inprocessor (164) or, typically much faster, by use dedicated ALU (170).

The example compute node (152) of FIG. 2 includes a direct memory access(‘DMA’) controller (195), which is computer hardware for direct memoryaccess and a DMA engine (195), which is computer software for directmemory access. Direct memory access includes reading and writing tomemory of compute nodes with reduced operational burden on the centralprocessing units (164). A DMA transfer essentially copies a block ofmemory from one compute node to another. While the CPU may initiates theDMA transfer, the CPU does not execute it. In the example of FIG. 2, theDMA engine (195) and the DMA controller (195) support the messagingmodule (161).

Although the explanation above with reference to FIG. 2 describes acompute node, readers will note that a service node may be similarlyconfigured and operate in the same manner as the compute node (152) forscheduling applications for execution on a plurality of compute nodes ofa parallel computer to manage temperature of the plurality of computenodes during execution according to embodiments of the presentinvention. That is, a service node may include one or more processors,computer memory, bus adapters and buses, communications adapters, and soon, each operatively coupled together to providing processing forcomputer program instructions stored in computer memory.

For further explanation, FIG. 3A illustrates an exemplary Point To PointAdapter (180) useful in systems capable of profiling an application forpower consumption during execution on a compute node according toembodiments of the present invention. Point To Point Adapter (180) isdesigned for use in a data communications network optimized for point topoint operations, a network that organizes compute nodes in athree-dimensional torus or mesh. Point To Point Adapter (180) in theexample of FIG. 3A provides data communication along an x-axis throughfour unidirectional data communications links, to and from the next nodein the −x direction (182) and to and from the next node in the +xdirection (181). Point To Point Adapter (180) also provides datacommunication along a y-axis through four unidirectional datacommunications links, to and from the next node in the −y direction(184) and to and from the next node in the +y direction (183). Point ToPoint Adapter (180) in FIG. 3A also provides data communication along az-axis through four unidirectional data communications links, to andfrom the next node in the −z direction (186) and to and from the nextnode in the +z direction (185).

For further explanation, FIG. 3B illustrates an exemplary GlobalCombining Network Adapter (188) useful in systems capable of profilingan application for power consumption during execution on a compute nodeaccording to embodiments of the present invention. Global CombiningNetwork Adapter (188) is designed for use in a network optimized forcollective operations, a network that organizes compute nodes of aparallel computer in a binary tree. Global Combining Network Adapter(188) in the example of FIG. 3B provides data communication to and fromtwo children nodes through four unidirectional data communications links(190). Global Combining Network Adapter (188) also provides datacommunication to and from a parent node through two unidirectional datacommunications links (192).

For further explanation, FIG. 4 sets forth a line drawing illustratingan exemplary data communications network (108) optimized for point topoint operations useful in systems capable of profiling an applicationfor power consumption during execution on a compute node in accordancewith embodiments of the present invention. In the example of FIG. 4,dots represent compute nodes (102) of a parallel computer, and thedotted lines between the dots represent data communications links (103)between compute nodes. The data communications links are implementedwith point to point data communications adapters similar to the oneillustrated for example in FIG. 3A, with data communications links onthree axes, x, y, and z, and to and fro in six directions +x (181), −x(182), +y (183), −y (184), +z (185), and −z (186). The links and computenodes are organized by this data communications network optimized forpoint to point operations into a three dimensional mesh (105). The mesh(105) has wrap-around links on each axis that connect the outermostcompute nodes in the mesh (105) on opposite sides of the mesh (105).These wrap-around links form part of a torus (107). Each compute node inthe torus has a location in the torus that is uniquely specified by aset of x, y, z coordinates. Readers will note that the wrap-around linksin the y and z directions have been omitted for clarity, but areconfigured in a similar manner to the wrap-around link illustrated inthe x direction. For clarity of explanation, the data communicationsnetwork of FIG. 4 is illustrated with only 27 compute nodes, but readerswill recognize that a data communications network optimized for point topoint operations for use in profiling an application for powerconsumption during execution on a compute node in accordance withembodiments of the present invention may contain only a few computenodes or may contain thousands of compute nodes.

For further explanation, FIG. 5 sets forth a line drawing illustratingan exemplary data communications network (106) optimized for collectiveoperations useful in systems capable of profiling an application forpower consumption during execution on a compute node in accordance withembodiments of the present invention. The example data communicationsnetwork of FIG. 5 includes data communications links connected to thecompute nodes so as to organize the compute nodes as a tree. In theexample of FIG. 5, dots represent compute nodes (102) of a parallelcomputer, and the dotted lines (103) between the dots represent datacommunications links between compute nodes. The data communicationslinks are implemented with global combining network adapters similar tothe one illustrated for example in FIG. 3B, with each node typicallyproviding data communications to and from two children nodes and datacommunications to and from a parent node, with some exceptions. Nodes ina binary tree (106) may be characterized as a physical root node (202),branch nodes (204), and leaf nodes (206). The root node (202) has twochildren but no parent. The leaf nodes (206) each has a parent, but leafnodes have no children. The branch nodes (204) each has both a parentand two children. The links and compute nodes are thereby organized bythis data communications network optimized for collective operationsinto a binary tree (106). For clarity of explanation, the datacommunications network of FIG. 5 is illustrated with only 31 computenodes, but readers will recognize that a data communications networkoptimized for collective operations for use in systems for profiling anapplication for power consumption during execution on a compute node inaccordance with embodiments of the present invention may contain only afew compute nodes or may contain thousands of compute nodes.

In the example of FIG. 5, each node in the tree is assigned a unitidentifier referred to as a ‘rank’ (250). A node's rank uniquelyidentifies the node's location in the tree network for use in both pointto point and collective operations in the tree network. The ranks inthis example are assigned as integers beginning with 0 assigned to theroot node (202), 1 assigned to the first node in the second layer of thetree, 2 assigned to the second node in the second layer of the tree, 3assigned to the first node in the third layer of the tree, 4 assigned tothe second node in the third layer of the tree, and so on. For ease ofillustration, only the ranks of the first three layers of the tree areshown here, but all compute nodes in the tree network are assigned aunique rank.

For further explanation, FIG. 6 sets forth a flow chart illustrating anexemplary method for profiling an application for power consumptionduring execution on a compute node according to embodiments of thepresent invention. Profiling an application for power consumption duringexecution on a compute node according to the method of FIG. 6 may becarried out by a power profiling module installed on a stand-alongcomputer or a service node of a parallel computer such as, for example,the service described above. Such a parallel computer may include aplurality of compute nodes in a parallel computer connected together fordata communications using a plurality of data communications networks.At least one of the data communications networks may be optimized forpoint to point operations, and at least one of the data communicationsmay be optimized for collective operations. The method of FIG. 6includes receiving (600) an application (200) for execution on a computenode. A service node may receive (600) an application (200) forexecution on a compute node according to the method of FIG. 6 byreceiving an identifier for the application (200) from a systemadministrator or a job file and retrieving the application (200)matching the application identifier from data storage. The data storagemay be directly connected to the service node through a storage driveadapter, indirectly connected through a network, or through any othermeans as will occur to those of skill in the art.

The method of FIG. 6 includes identifying (602) a hardware powerconsumption profile (142) for the compute node. The hardware powerconsumption profile (142) of FIG. 6 represents a data structure thatspecifies power consumption for compute node hardware during performanceof various processing operations (606). In such a manner, the hardwarepower consumption profile (142) provides power consumption informationof various processing operations for platform-specific hardware. Thehardware power consumption profile (142) may be used to translateprocessing operations specified by the application (200) into powerconsumption information for those processing operations when performedby the platform-specific hardware. Using different hardware powerconsumption profiles for different compute nodes, therefore, allows aservice node to determine the different power consumptions that wouldoccur by executing the application (200) on different compute nodes.

In the example of FIG. 6, each record of the hardware power consumptionprofile (142) specifies a processing operation (606) and a predefinedpower consumption value (608). As mentioned above, a processingoperation (606) in the example of FIG. 6 is a task specified by anapplication or performed by a compute node in response to a taskspecified by an application. Such a task may be a single instruction ora set of multiple instructions. For example, a processing operation maybe implemented as a single float multiply-add instruction or a series ofinstructions processed when a cache miss occurs during applicationexecution. In the example of FIG. 6, a predefined power consumptionvalue (608) is a value represents the power consumption by a computenode when the associated processing operation is performed by thecompute node. The predefined power consumption value (608) of FIG. 6 maybe specified in units of Watts, some other units of power, or in theunits of some other quantity having a known relationship to power suchas, for example, energy, CPU cycles, and so on. The power consumptionvalue (608) of FIG. 6 is ‘predefined’ in the sense that previousmeasurements or estimation is used to determine the power consumption ofperforming a particular processing operation on a particular computenode. For further explanation of the hardware power consumption profile(142), consider the following exemplary hardware power consumptionprofile:

TABLE 1 EXEMPLARY HARDWARE POWER CONSUMPTION PROFILE PROCESSINGPREDEFINED POWER OPERATION CONSUMPTION VALUE integer-add  80 Wattsbranch 120 Watts float-multiply-add 150 Watts . . . . . .

Table 1 above illustrates an exemplary hardware power consumptionprofile for a particular compute node. The exemplary hardware powerconsumption profile above specifies that the compute node consumes 80Watts of power while performing an addition operation on integers. Inaddition, the exemplary hardware power consumption profile abovespecifies that the compute node consumes 120 Watts of power whileperforming a branch operation. The exemplary hardware power consumptionprofile above also specifies that the compute node consumes 150 Watts ofpower while performing a multiply-add operation on floating pointvalues. Readers will note that the exemplary hardware power consumptionprofile illustrated above is for explanation only and not forlimitation, but other hardware power consumption profiles may also beuseful in exemplary embodiments of the present invention.

The method of FIG. 6 also includes determining (604) a power consumptionprofile (144) for the application (200) in dependence upon theapplication (200) and the hardware power consumption profile (142) forthe compute node. Because different portions of an application mayutilize compute node hardware in different ways, different levels ofpower consumption typically occur during different portions of theapplication's execution sequence. The power consumption profile (144) ofFIG. 6 for the application (200) represents a data structure thatassociates power consumption with particular portions of the application(200). In the example of FIG. 6, each record of the power consumptionprofile (144) specifies a particular portion of the application (200)using an application portion identifier (616) and specifies a powerconsumption (618) associated with that application portion. Theapplication portion may be implemented as a single processing operationof the application or a group of processing operations such as, forexample, a function or an application procedure. As mentioned above, thepower consumption (618) of FIG. 6 represents the level of power consumedduring execution of the portion of the application (200) associated withthe power consumption in the power consumption profile record.

For further explanation, consider the following exemplary powerconsumption profile for an exemplary application:

TABLE 2 EXEMPLARY POWER CONSUMPTION PROFILE APPLICATION POWER PORTION IDCONSUMPTION AAA00000h:AAAO1FFFh  90 Watts AAA02000h:AAA03AFFh 280 WattsAAA03B00h:AAA04FFFh 150 Watts . . . . . .

Table 2 above illustrates an exemplary power consumption profile for anapplication to be executed on a compute node. The exemplary powerconsumption profile above specifies that the portion of the applicationat memory address range ‘AAA00000h:AAA01FFFh’ consumes on average 90Watts of power. The exemplary power consumption profile above specifiesthat the portion of the application at memory address range‘AAA02000h:AAA03AFFh’ consumes on average 280 Watts of power. Theexemplary power consumption profile above specifies that the portion ofthe application at memory address range ‘AAA03B00h:AAA04FFFh’ consumeson average 150 Watts of power. The exemplary power consumption profileabove uses address ranges to specify portion of an application, butreaders will note that line numbers in application source code files mayalso be used along with any other implementation as will occur to thoseof skill in the art. Readers will also note generally that the exemplarypower consumption profile illustrated above is for explanation only andnot for limitation and that other power consumption profiles may also beuseful in exemplary embodiments of the present invention.

In the method of FIG. 6, determining (604) a power consumption profile(144) for the application (200) includes identifying (610) one or moreprocessing operations (612) specified by the application (200) andlooking up (614), for each processing operation (612) specified by theapplication (200), a predefined power consumption value (608) for thatprocessing operation (606) in the hardware power consumption profile(142). In the example of FIG. 6, identifying (610) one or moreprocessing operations (612) specified by the application (200) andlooking up (614), for each processing operation (612) specified by theapplication (200), a predefined power consumption value (608) for thatprocessing operation (606) may be carried out on a portion-by-portionbasis for the application. In such a manner, determining (604) a powerconsumption profile (144) for the application (200) according to themethod of FIG. 6 may also include averaging the power consumption forall of the processing operations in a particular application portion andassociating the average power consumption with that application portionin the power consumption profile (144).

The method of FIG. 6 includes reporting (620) the power consumptionprofile (144) for the application (200). Reporting (620) the powerconsumption profile (144) for the application (200) according to themethod of FIG. 6 may be carried out by rendering the power consumptionprofile (144) on a user interface to a system administrator,transmitting the power consumption profile (144) to a systemadministrator in a message, or storing the power consumption profile(144) in data storage and notifying a system administrator of theprofile's data storage location. In some other embodiments, reporting(620) the power consumption profile (144) for the application (200)according to the method of FIG. 6 may also be carried out by annotatingthe source code version of the application (200) with the powerconsumption for each portion of the application (144). Annotating thesource code version of the application (200) may be carried out bytranslating instruction addresses of the executable image of theapplication (200) into source code line numbers using compilationinformation generated by the compiler used to create the application'sexecutable image from the source code.

The explanation above with reference to FIG. 6 describes profiling anapplication for power consumption without having to actually execute theapplication. The application need not be executed because the hardwarepower consumption profile may describe the power consumption on aparticular hardware platform for the processing operations included inthe application. In some other embodiments, however, an applicationpower consumption profile may be generated based on the processingoperations actually performed by a compute node during execution of theapplication—including those operations not specifically specified by theapplication but that occur during application execution such as, forexample, operating system error handlers, cache miss operations, and soon. For further explanation, therefore, FIG. 7 sets forth a flow chartillustrating a further exemplary method for profiling an application forpower consumption during execution on a compute node according toembodiments of the present invention.

The method of FIG. 7 is similar to the method of FIG. 6. That is, themethod of FIG. 7 includes: receiving (600) an application (200) forexecution on a compute node; identifying (602) a hardware powerconsumption profile (142) for the compute node, the hardware powerconsumption profile (142) specifying power consumption for compute nodehardware during performance of various processing operations (702);determining (604) a power consumption profile (144) for the application(200) in dependence upon the application (200) and the hardware powerconsumption profile (142) for the compute node; and reporting (620) thepower consumption profile (144) for the application (200). In theexample of FIG. 7, each record of the hardware power consumption profile(142) specifies an executed processing operation (702) and a powerconsumption (704) that occurs when the associated processing operation(702) is executed. Each record of the power consumption profile (144) ofFIG. 7 specifies a particular portion of the application (200) using anapplication portion identifier (616) and specifies power consumption(618) associated with that application portion.

In the method of FIG. 7, however, determining (604) a power consumptionprofile (144) for the application (200) includes: identifying (700) oneor more processing operations (706) specified by the application (200)and monitoring (708), for each processing operation (706), executionperformance (710) during execution of that processing operation (706).The execution performance (710) of FIG. 7 describes the processingoperations actually executed on the compute node when the compute nodeexecutes a processing operation (706) specified by the application(200). Readers will note that the processing operations (706) specifiedby the application (200) are not the only processing operations thatoccur during execution of the application. For example, during executionof a processing operation specified by the application to load data frommemory, the operating system may attempt to retrieve the data from cacheand encounter a cache miss. In such an example, additional operatingsystem processing operations for handling a cache miss are executed thatwould otherwise have not occurred had the data been stored in the cache.

Determining (604) a power consumption profile (144) for the application(200) according to the method of FIG. 7 also includes estimating (712),for each processing operation (706) using the hardware power consumptionprofile (142), power consumption for that processing operation independence upon the execution performance (710) for that processingoperation (706). Estimating (712) power consumption for that processingoperation according to the method of FIG. 7 may be carried out bylocating values for power consumption (704) associated in the hardwarepower consumption profile (142) with the executed processing operations(702) identified when monitoring the execution performance (710) of theapplication (200). Similar to the method of FIG. 6, determining (604)power consumption profile (144) for the application (200) according tothe method of FIG. 7 may be carried out on a portion-by-portion basisfor the application (200). In such a manner, determining (604) a powerconsumption profile (144) for the application (200) according to themethod of FIG. 7 may also include averaging the power consumption forall of the processing operations in a particular application portion andassociating the average power consumption with that application portionin the power consumption profile (144).

Using a power consumption profile for an application, an applicationdeveloper may discover that certain portions of the application consumepower at undesirable levels. In such cases, the application developermay customize the application for a specific hardware platform in aneffort to reduce overall application power consumption or peak powerconsumption levels for the application. For further explanation,therefore, FIG. 8 sets forth a flow chart illustrating a furtherexemplary method for profiling an application for power consumptionduring execution on a compute node according to embodiments of thepresent invention.

The method of FIG. 8 is similar to the method of FIG. 6. That is, themethod of FIG. 8 includes: receiving (600) an application (200) forexecution on a compute node; identifying (602) a hardware powerconsumption profile (142) for the compute node, the hardware powerconsumption profile (142) specifying power consumption for compute nodehardware during performance of various processing operations;determining (604) a power consumption profile (144) for the application(200) in dependence upon the application (200) and the hardware powerconsumption profile (142) for the compute node; and reporting (620) thepower consumption profile (144) for the application (200).

The method of FIG. 8 also includes altering (800) the application (200)in dependence upon the power consumption profile (144) for theapplication (200). Altering (800) the application (200) according to themethod of FIG. 8 may be carried out by an application developer (806).An application developer (806) may alter (800) the application (200)according to the method of FIG. 8 by comparing the power consumption inthe power consumption profile (144) for various portions of theapplication (200) with a power consumption threshold and modifying theportions of the application for which the power consumption exceeds thepower consumption threshold to reduce power consumption during executionof those portions of the application (200). Such a power consumptionthreshold may be the same value for each portion of the application orbe different for different portions of the application. Additionally,the power consumption threshold may be a static threshold or one thatchanges dynamically based on the environment such as, for example, thepower consumption of the other applications running on the compute nodeor the price of electricity. Readers will note that in addition to beingcarried out by an application developer, computer software may also beused to alter (800) the application (200) in dependence upon the powerconsumption profile (144) for the application (200). Using templates andvarious rulesets, the software could replace certain applicationportions that consume too much power with lower power consuming sets ofprocessing operations.

The method of FIG. 8 also includes executing (804) the alteredapplication (802) on the compute node. Executing (804) the alteredapplication (802) on the compute node according to the method of FIG. 8may be carried out by transferring the application (200) to the computenode and instructing the operating system on the compute node toschedule the application (200) for execution on the processors of thecompute node.

Exemplary embodiments of the present invention are described largely inthe context of a fully functional computer system for profiling anapplication for power consumption during execution on a compute node.Readers of skill in the art will recognize, however, that the presentinvention also may be embodied in a computer program product disposed oncomputer readable media for use with any suitable data processingsystem. Such computer readable media may be transmission media orrecordable media for machine-readable information, including magneticmedia, optical media, or other suitable media. Examples of recordablemedia include magnetic disks in hard drives or diskettes, compact disksfor optical drives, magnetic tape, and others as will occur to those ofskill in the art. Examples of transmission media include telephonenetworks for voice communications and digital data communicationsnetworks such as, for example, Ethernets™ and networks that communicatewith the Internet Protocol and the World Wide Web as well as wirelesstransmission media such as, for example, networks implemented accordingto the IEEE 802.11 family of specifications. Persons skilled in the artwill immediately recognize that any computer system having suitableprogramming means will be capable of executing the steps of the methodof the invention as embodied in a program product. Persons skilled inthe art will recognize immediately that, although some of the exemplaryembodiments described in this specification are oriented to softwareinstalled and executing on computer hardware, nevertheless, alternativeembodiments implemented as firmware or as hardware are well within thescope of the present invention.

It will be understood from the foregoing description that modificationsand changes may be made in various embodiments of the present inventionwithout departing from its true spirit. The descriptions in thisspecification are for purposes of illustration only and are not to beconstrued in a limiting sense. The scope of the present invention islimited only by the language of the following claims.

What is claimed is:
 1. A method of profiling an application for powerconsumption during execution on a plurality of compute nodes, theplurality compute nodes connected together using a plurality of datacommunications networks, at least one data communications networkoptimized for collective operations, at least one data communicationsnetwork optimized for point to point operations, the method comprising:identifying a hardware power consumption profile for a compute node, thehardware power consumption profile specifying power consumption forcompute node hardware during performance of various processingoperations; determining a power consumption profile for an applicationto be executed on the compute node in dependence upon the applicationand the hardware power consumption profile for the compute node.
 2. Themethod of claim 1 further comprising altering the application independence upon the power consumption profile for the application. 3.The method of claim 2 further comprising executing the alteredapplication on the compute node.
 4. The method of claim 1 whereindetermining a power consumption profile for the application independence upon the application and the hardware power consumptionprofile for the compute node further comprises: identifying one or moreprocessing operations specified by the application; and looking up, foreach processing operation specified by the application, a predefinedpower consumption value for that processing operation in the hardwarepower consumption profile.
 5. The method of claim 1 wherein determininga power consumption profile for the application in dependence upon theapplication and the hardware power consumption profile for the computenode further comprises: identifying one or more processing operationsspecified by the application; monitoring, for each processing operation,execution performance during execution of that processing operation; andestimating, for each processing operation using the hardware powerconsumption profile, power consumption for that processing operation independence upon the execution performance for that processing operation.6. An apparatus capable of profiling an application for powerconsumption during execution on a plurality of compute nodes, theplurality compute nodes connected together using a plurality of datacommunications networks, at least one data communications networkoptimized for collective operations, at least one data communicationsnetwork optimized for point to point operations, the apparatus one ormore computer processors and computer memory operatively coupled to thecomputer processors, the computer memory having disposed within itcomputer program instructions that, when executed by the computerprocessor cause the apparatus to carry out the steps of: identifying ahardware power consumption profile for a compute node, the hardwarepower consumption profile specifying power consumption for compute nodehardware during performance of various processing operations;determining a power consumption profile for an application to beexecuted on the compute node in dependence upon the application and thehardware power consumption profile for the compute node.
 7. Theapparatus of claim 6 wherein the computer memory has disposed within itcomputer program instructions that, when executed by the computerprocessor, causes the apparatus to carry out the step of altering theapplication in dependence upon the power consumption profile for theapplication.
 8. The apparatus of claim 7 wherein the computer memory hasdisposed within it computer program instructions that, when executed bythe computer processor, causes the apparatus to carry out the step ofexecuting the altered application on the compute node.
 9. The apparatusof claim 6 wherein determining a power consumption profile for theapplication in dependence upon the application and the hardware powerconsumption profile for the compute node further comprises: identifyingone or more processing operations specified by the application; andlooking up, for each processing operation specified by the application,a predefined power consumption value for that processing operation inthe hardware power consumption profile.
 10. The apparatus of claim 6wherein determining a power consumption profile for the application independence upon the application and the hardware power consumptionprofile for the compute node further comprises: identifying one or moreprocessing operations specified by the application; monitoring, for eachprocessing operation, execution performance during execution of thatprocessing operation; and estimating, for each processing operationusing the hardware power consumption profile, power consumption for thatprocessing operation in dependence upon the execution performance forthat processing operation.
 11. A computer program product for profilingan application for power consumption during execution on a plurality ofcompute nodes, the plurality compute nodes connected together using aplurality of data communications networks, at least one datacommunications network optimized for collective operations, at least onedata communications network optimized for point to point operations, thecomputer program product disposed upon a computer readablenon-transmission medium, the computer program product comprisingcomputer program instructions that, when executed by a computer, carryout the steps of: identifying a hardware power consumption profile for acompute node, the hardware power consumption profile specifying powerconsumption for compute node hardware during performance of variousprocessing operations; determining a power consumption profile for anapplication to be executed on the compute node in dependence upon theapplication and the hardware power consumption profile for the computenode.
 12. The computer program product of claim 11 further comprisingcomputer program instructions that, when executed by the computer, carryout the step of altering the application in dependence upon the powerconsumption profile for the application.
 13. The computer programproduct of claim 12 further comprising computer program instructionsthat, when executed by the computer, carry out the step of executing thealtered application on the compute node.
 14. The computer programproduct of claim 11 wherein determining a power consumption profile forthe application in dependence upon the application and the hardwarepower consumption profile for the compute node further comprises:identifying one or more processing operations specified by theapplication; and looking up, for each processing operation specified bythe application, a predefined power consumption value for thatprocessing operation in the hardware power consumption profile.
 15. Thecomputer program product of claim 11 wherein determining a powerconsumption profile for the application in dependence upon theapplication and the hardware power consumption profile for the computenode further comprises: identifying one or more processing operationsspecified by the application; monitoring, for each processing operation,execution performance during execution of that processing operation; andestimating, for each processing operation using the hardware powerconsumption profile, power consumption for that processing operation independence upon the execution performance for that processing operation.